Spaces:
Runtime error
Runtime error
File size: 6,921 Bytes
5a7ab71 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 |
import dataclasses
import gc
import glob
import os
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
import torch
from torch import Tensor
import torch.nn as nn
from torch.nn import functional as F
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
@dataclasses.dataclass
class CompressionConfig:
"""Group-wise quantization."""
num_bits: int
group_size: int
group_dim: int
symmetric: bool
enabled: bool = True
default_compression_config = CompressionConfig(
num_bits=8, group_size=256, group_dim=1, symmetric=True, enabled=True
)
class CLinear(nn.Module):
"""Compressed Linear Layer."""
def __init__(self, weight=None, bias=None, device=None):
super().__init__()
if weight is None:
self.weight = None
elif isinstance(weight, Tensor):
self.weight = compress(weight.data.to(device), default_compression_config)
else:
self.weight = weight
self.bias = bias
def forward(self, input: Tensor) -> Tensor:
weight = decompress(self.weight, default_compression_config)
return F.linear(input.to(weight.dtype), weight, self.bias)
def compress_module(module, target_device):
for attr_str in dir(module):
target_attr = getattr(module, attr_str)
if type(target_attr) == torch.nn.Linear:
setattr(
module,
attr_str,
CLinear(target_attr.weight, target_attr.bias, target_device),
)
for name, child in module.named_children():
compress_module(child, target_device)
def get_compressed_list(module, prefix=''):
compressed_list = []
for attr_str in dir(module):
target_attr = getattr(module, attr_str)
if type(target_attr) == torch.nn.Linear:
full_name = f"{prefix}.{attr_str}.weight" if prefix else f"{attr_str}.weight"
compressed_list.append(full_name)
for name, child in module.named_children():
child_prefix = f"{prefix}.{name}" if prefix else name
for each in get_compressed_list(child, child_prefix):
compressed_list.append(each)
return compressed_list
def apply_compressed_weight(module, compressed_state_dict, target_device, prefix=''):
for attr_str in dir(module):
target_attr = getattr(module, attr_str)
if type(target_attr) == torch.nn.Linear:
full_name = f"{prefix}.{attr_str}.weight" if prefix else f"{attr_str}.weight"
setattr(module, attr_str,
CLinear(compressed_state_dict[full_name], target_attr.bias, target_device))
for name, child in module.named_children():
child_prefix = f"{prefix}.{name}" if prefix else name
apply_compressed_weight(child, compressed_state_dict, target_device, child_prefix)
def load_compress_model(model_path, device, torch_dtype):
# partially load model
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
base_pattern = os.path.join(model_path, "pytorch_model-*.bin")
files = glob.glob(base_pattern)
with init_empty_weights():
config = AutoConfig.from_pretrained(model_path, low_cpu_mem_usage=True,
torch_dtype=torch_dtype)
model = AutoModelForCausalLM.from_config(config)
linear_weights = get_compressed_list(model)
compressed_state_dict = {}
for filename in tqdm(files):
tmp_state_dict = torch.load(filename)
for name in tmp_state_dict:
if name in linear_weights:
tensor = tmp_state_dict[name].to(device).data.to(torch_dtype)
compressed_state_dict[name] = compress(tensor, default_compression_config)
else:
compressed_state_dict[name] = tmp_state_dict[name].to(device)
tmp_state_dict[name] = None
tensor = None
gc.collect()
torch.cuda.empty_cache()
for name in model.state_dict():
if name not in linear_weights:
set_module_tensor_to_device(model, name, device, value=compressed_state_dict[name])
apply_compressed_weight(model, compressed_state_dict, device)
model.to(device)
return model, tokenizer
def compress(tensor, config):
"""Simulate group-wise quantization."""
if not config.enabled:
return tensor
group_size, num_bits, group_dim, symmetric = (
config.group_size,
config.num_bits,
config.group_dim,
config.symmetric,
)
assert num_bits <= 8
original_shape = tensor.shape
num_groups = (original_shape[group_dim] + group_size - 1) // group_size
new_shape = (
original_shape[:group_dim]
+ (num_groups, group_size)
+ original_shape[group_dim + 1 :]
)
# Pad
pad_len = (group_size - original_shape[group_dim] % group_size) % group_size
if pad_len != 0:
pad_shape = (
original_shape[:group_dim] + (pad_len,) + original_shape[group_dim + 1 :]
)
tensor = torch.cat(
[tensor, torch.zeros(pad_shape, dtype=tensor.dtype, device=tensor.device)],
dim=group_dim,
)
data = tensor.view(new_shape)
# Quantize
if symmetric:
B = 2 ** (num_bits - 1) - 1
scale = B / torch.max(data.abs(), dim=group_dim + 1, keepdim=True)[0]
data = data * scale
data = data.clamp_(-B, B).round_().to(torch.int8)
return data, scale, original_shape
else:
B = 2**num_bits - 1
mn = torch.min(data, dim=group_dim + 1, keepdim=True)[0]
mx = torch.max(data, dim=group_dim + 1, keepdim=True)[0]
scale = B / (mx - mn)
data = data - mn
data.mul_(scale)
data = data.clamp_(0, B).round_().to(torch.uint8)
return data, mn, scale, original_shape
def decompress(packed_data, config):
"""Simulate group-wise dequantization."""
if not config.enabled:
return packed_data
group_size, num_bits, group_dim, symmetric = (
config.group_size,
config.num_bits,
config.group_dim,
config.symmetric,
)
# Dequantize
if symmetric:
data, scale, original_shape = packed_data
data = data / scale
else:
data, mn, scale, original_shape = packed_data
data = data / scale
data.add_(mn)
# Unpad
pad_len = (group_size - original_shape[group_dim] % group_size) % group_size
if pad_len:
padded_original_shape = (
original_shape[:group_dim]
+ (original_shape[group_dim] + pad_len,)
+ original_shape[group_dim + 1 :]
)
data = data.reshape(padded_original_shape)
indices = [slice(0, x) for x in original_shape]
return data[indices].contiguous()
else:
return data.view(original_shape)
|